Science Journal of Energy Engineering 2017; 5(2): 40-47

http://www.sciencepublishinggroup.com/j/sjee

doi: 10.11648/j.sjee.20170502.11

ISSN: 2376-810X (Print); ISSN: 2376-8126 (Online)

Analysis of Failure Modes Effect and Criticality Analysis (FMECA): A Stand-Alone Photovoltaic System

Omar Ngala Sarr, Fabe Idrissa Barro, Oumar Absatou Niasse, Fatou Dia, Nacir Mbengue,

Bassirou Ba, Cheikh Sene

Department of Physics, Faculty of Science and Technology, Semiconductors and Solar Energy Laboratory - Cheikh Anta Diop University,

Dakar, Senegal

Email address: sarromarngala@gmail.com (O. N. Sarr)

To cite this article: Omar Ngala Sarr, FABE Idrissa Barro, Oumar Absatou Niasse, Fatou Dia, Nacir Mbengue, Bassirou Ba, Cheikh SENE. Analysis of Failure

Modes Effect and Criticality Analysis (FMECA): A Stand-Alone Photovoltaic System. Science Journal of Energy Engineering.

Vol. 5, No. 2, 2017, pp. 40-47. doi: 10.11648/j.sjee.20170502.11

Received: February 24, 2017; Accepted: March 8, 2017; Published: March 27, 2017

Abstract: This study deals with the implementation of a methodological guide for the maintenance of photovoltaic systems in

Senegal. Typical PV systems components are photovoltaic panels, and inverter, a regulator, connecting cables and the battery; so

Failure Modes Effect and Criticality Analysis (FMECA) is performed on the PV system in order to increase the reliability and

reduce system failures. To do that, a functional analysis of the system through an octopus diagram and a dysfunctional analysis

through a fault tree, are used as a decision support for the choice of the coefficients to obtain the full system FMEA. The obtained

results allowed us to detect about 40% of the types of failure that cause over 60% of system malfunction. Anticipating these types

of failure through preventive maintenance would make the PV system more reliable.

Keywords: FMECA, Photovoltaic Systems, Maintenance

1. Introduction

In the actual context of sustainable development.

Renewable energies are undoubtedly an ideal solution from

their availability and their perennity. That explains surely all

works carried out in renewable energies and particularly

photovoltaic. Photovoltaic solar energy represents a factor

impossible to circumvent in the race with energies in Africa

and particularly in Senegal; however, there is a lack of about

maintenance on PV systems. It is then of prior importance to

fill this gap. For example, practically 80% of the photovoltaic

street lamps does not function practically more than two to

three months. It remains obvious that it is not the solar

illumination, which is lacking, but rather a bad installation or a

poor maintenance. Moreover this remains also valid for

photovoltaic power plant. It is then a great interest to set a

system for the maintenance of photovoltaic power plants in

Senegal. The main objectives of this work is then to analyze

the failure mode in PV systems and then apply FMECA

method to set up or improve the maintenance of those systems.

2. Analysis of the Modes of Failure

2.1. The Reliability of a System

� Reliability

The reliability is the ability of an entity to perform the

required functions under stated conditions for a specified time

[5]. It is characterized by the probability R (t) the entity E

accomplish these functions under the conditions given for the

time interval [0, t], given that the entity is not broken at the

time t=0, see figure 1.

R (t) = P [E not defaulting on [0, t]]

Reliability is often modeled by:

R(t)=exp(-λt) (1)

Where λ is the failure rate expressed as the percentage of

defects

41 Omar Ngala Sarr et al.: Analysis of Failure Modes Effect and Criticality Analysis (FMECA):

A Stand-Alone Photovoltaic System

Figure 1. Reliability.

� Availability of a system Availability is the ability of an entity to be able to

accomplish the functions required under the given conditions

and at a given time [1]. It is characterized by the probability A

(t) of the entity E at time t, to perform the duties required

under given conditions.

A (t) = P [E non-defaulting at time t]

� Maintainability of a system

Maintainability is the ability of an entity to be maintained or

restored to a state in which it can perform a required function,

when maintenance is performed under given conditions with

prescribed procedures and resources. It is characterized by the

probability M (t) the entity E is in state at time t, to perform his

duties, knowing that the entity was not working at time t = 0

[1].

M (t) = P [E is repaired on [0, t]]

� Safety

Safety is the ability of an entity to avoid, under given

conditions, critical or catastrophic events. It is characterized

by the probability S (t) that the entity E does not let appear in

given conditions, critical or catastrophic events.

S (t) = P [E avoids critical or catastrophic events on [0, t]]

� Means reliability time

Figure 2 shows schematically the successive states possible

for a repairable system [1].

Figure 2. Means reliability time.

In fact, the magnitudes carried by the graph are the

durations (TBF) to which there corresponds the means (MTBF)

obtained by static operation m (t) or probabilistic E (t) of the n

periods recorded and saved. The acronyms used correspond to

the following concepts:

� MTTF (Mean Time To Failure)

���� � � �����

� (2)

� MTTR (Mean Time To Repair)

���� � � 1 � ������

� (3)

� MTBF (Mean Time Between Failures)

���� � ��� � ��� (4)

� MUT (Mean Up Time)

� MDT (Mean Down Time)

2.2. Failure Rate

The instantaneous failure rate, λ (t) is a feature of reliability.

The λ value (t) dt is the conditional probability of a failure in

the time interval [t, t + dt], knowing that there is no failure in

the time interval [0, t]. Thus, applying the theorem of

conditional probabilities, then λ (t) is so that:

Figure 3. Failure rate.

2.3. Exponential Distribution

The exponential distribution is most commonly used in

electronics to describe the reliability period during which the

equipment failure rate is considered constant (random failure).

It describes the elapsed time to failure, or the time interval

between two failures. It is defined by a single parameter, the

failure rate, λ. It is characterized by:

� The Reliability:

��� � ����� (5)

y

Science Journal of Energy Engineering 2017; 5(2): 40-47 42

� The probability density:

��� � ���� (6)

� The failure rate:

��� � � (7)

� MTBF (Mean Time Between Failure):

���� ��

� (8)

The system

The studied system consists of a solar array connected to a

controller, a battery bank and inverter (figure 4)

Figure 4. The system.

2.4. Functional Analysis Through an Octopus Diagram

The system whose failures are studied must first be

"shelled".

What is it used for? What functions does it fulfill? How

does it work? Functional analysis must answer these questions

rigorously. The system is analyzed under two aspects:

� External: relationships with the external environment.

� Internal: analysis of flows and activities within the

process.

Figure 5. The octopus diagram.

2.5. Fault Tree Analysis

The fault tree comprises a plurality of branching describes

the probable causes of an event may occur. These events are

the result of a number of other events connected by logic

"AND" and "Or" gates.

43 Omar Ngala Sarr et al.: Analysis of Failure Modes Effect and Criticality Analysis (FMECA):

A Stand-Alone Photovoltaic System

Figure 6. Fault tree connection.

In figure 6 the event E1 resulting from the connection of

events e1 and e2 through an OR gate, can only happen if one

of e1 and e2 occurs well or both events occur simultaneously.

Contrary, event E2 will happen only if e3 and e4.

In our system, the feared event is that the PV system does

not supply power. There are three major events and their

possible causes, which are described by the fault tree. These

events include lack of energy output of the AC cable following

a minor failure, or following a major failure and lack of energy

output of the inverter. These events result from the causes

described in the following tree.

Figure 7. System failure tree.

2.6. Failure Modes, Effects and Criticality Analysis

(FMECA)

The purpose of the FMECA is to highlight the most critical

failures in order to control them. This is an estimation of the

criticality index because the trio-mode-effect of potential

failure studied according to certain criteria. Several criteria

can be used to determine this index. A failure is even more

important if:

� The consequences are serious;

� It is quite common;

� If that detection is uncertain

Criticality is the product of the rating assigned to each of the

criteria. In this study the notes range from 1 to 10 for each

criterion. The allocation of these points is described by the

following tables.

� Occurrence

The occurrence is the probability of the failure mode,

estimated by answering the question: «What is the relative

probability of occurrence of this failure mode?».

Table 1. Occurrence listing.

Occurrence occurrence listing

Never or rarely appeared 1-2

Rarely appeared 3-4

That may appear or have appeared 5-6

already seen regularly appearance 7-8

Almost certain probability of occurrence 9-10

No energy output of the photovoltaic

system

Critical failure of AC cable

Minor failure of AC power cable and low input

No power output from the inverter

AC

cable cut

Melting AC cable

Significant corrosion of the

AC cable connectors

Défaut d'isolement

du câble AC

Minor cable failure

Degraded in energy output of the inverter

Energy degraded output of the AC cable

Critical failure of the inverter

Low input power

No energy output of the DC cable

Relay output failure

Failed

self test

GFCI protection

fault

Internal communication error

DC Bus too high

EEPROM failure

Failure importance of MPPT

Energy degraded output of the DC cable

Minor corrosion of the AC cable connectors

Degraded in energy output of the module

Insulated terminals

interconnections

Minor discoloration encapsulation

Minor corrosion

of the module

Insulated terminals

cells

Minor

damage Weld interconnections

Science Journal of Energy Engineering 2017; 5(2): 40-47 44

� Severity

This is to seek the prioritization of the severity of effects, by

answering the question: «What is the relative severity of that

effect?».

Table 2. Severity rating.

customer complaints Severity rating

Ineffective or not perceptible effect by the customer 1

Small inconvenience for the customer 2

Noticeable discomfort to the client without much too

much inconvenience, the solution can be found quickly 3-4

Significant inconvenience perceived by the customer 5-6

Total loss of function 7-8

Non- compliance 9

Security problem for the end user customer 10

� Undetected:

Failure detection is the probability of not detecting the

failure mode, by answering the question: «If the failure mode

occurs, what is the relative efficiency of detection means in

the current or proposed surveillance plan?» We must quoting

here the effectiveness of the monitoring plan or control plan,

whether actual or proposed.

Table 3. Undetected listing.

Undetected Undetected listing

Tests and planned trials will some detection 1-2

The detection by tests and planned trials is uncertain 3-4

Testing and planned tests do not guarantee detection 5-6

Detection is difficult 7-8

No tests or assays for detection 9-10

Calculation of criticality

Simply multiply the three quotations previously allocated

for determining priority C:

� � � × × �

C: criticality

S: Severity

U: undetected

The following table (table 4) shows the calculation of criticality

coefficients for each failure mode. The choice is based on the

previous tables. In the table, each element of the system is listed,

cited his failure mode and the detection mode presented. Criticality

is the product of the occurrence, severity and non- detection.

Table 4. Criticality coefficients.

Element Function methods

Causes Effects Detection Criticality

failures 0 S U C

PV Module

Convert solar energy

into electrical

energy

The PV module does

not supply electric

power

hotspots

The system does not

produce electric

energy

Open circuit

voltage

measurement

6 9 3 162

Failure of the junction box 7 9 7 441

broken Glass 4 8 4 128

Failure of the bypass diode module 3 7 5 105

delamination 4 7 6 168

Notorious decrease in

delivered power (less

than the maximum

power)

Broken cells / micro cracks

The system

produces less or no

electrical energy

Visual

8 5 7 280

Failure of the weld ribbons 6 7 8 336

broken interconnections 6 6 7 252

Discoloration of encapsulation 8 7 7 392

Corrosion 8 9 5 360

Inverter

Transforming

electric power to AC

power

The inverter does not

deliver an alternative

electric energy

Failure of Microcontroller

The system does not

produce electric

energy

visual

indicator

5 5 8 200

Failure GFCI protection 4 7 9 252

Internal communication

malfunctioning 4 7 5 140

Internal communication error 7 6 7 294

Failure of the output relays 4 6 8 192

DC BUS voltage too high 4 7 8 224

Failed Self-Test 6 6 8 288

Cable Ensure the flow of

electricity

The electrical energy

is not transmitted cable cut The system does not

produce electric

energy

Visual

5 7 2 70

The transmitted power

is low

melting cable (UV, heat,...) 4 9 6 216

Corrosion of connectors 6 7 2 84

Battery Store energy for a

return to the system

sulfation

repetitive discharge The battery does not

restore energy Visual

8 9 4 288

Hardening of lead sulfate crystals of

electrodes 6 7 4 168

Drying of the

electrolyte

Evaporation or leakage of

electrolyte

The battery does not

restore energy Visual 7 8 3 168

Regulator

Regulate the

charging and

discharging of the

battery for

protection of both

battery and loads

The controller does

not control the

charging and

discharging

Faulty control unit

the battery is not

loaded properly

indicator

light 4 7 5 140

The transistor does not work

Global

measurement

parameters

4 4 8 128

Blown fusible Visual 3 3 2 18

Failure of MPPT voltage

measurement 3 5 7 105

45 Omar Ngala Sarr et al.: Analysis of Failure Modes Effect and Criticality Analysis (FMECA):

A Stand-Alone Photovoltaic System

For a more detailed overview of the calculation, each

element will be studied separately and Pareto study will locate

types of failure on which the priorities will be.

3. Analysis of Results of the FMECA

Study of the System

Calculating coefficients allowed to find for each item

different types of failure and their values in terms of

criticality.

3.1. Criticality of Cable

The figure 8 shows the mapped criticality for the Cable.

Figure 8. Criticality of the cable elements.

For the cables, the listed potential failure are breaking,

melting or corrosion of connectors. Greater relative criticality

is noted to melting cables that combines a total of 216 points.

3.2. Criticality of the Elements of the Inverter

The figure 9 shows the criticality traced for the inverter.

Figure 9. Criticality of the elements of the Inverter.

The inverter includes alone about seven opportunities

failures. The internal communication error, the failure of

GFCI protection and the failed self-test total respectively 294,

252 and 288 points on the criticality level.

3.3. Criticality of the Elements of the Battery

The figure 10 is a graphical representation of criticality for

the battery.

Figure 10. Criticality of the Battery elements.

The three failure modes chosen for the battery are repetitive

discharges, sulfating and evaporation or leakage of the

electrolyte. Their critical points exceed 150 points.

3.4. Criticality of the Elements of the Regulator

Figure 11. Criticality of the elements of the Regulator.

The figure 11 shows the criticality map of the regulator

For the regulator, the faulty control unit has the highest

criticality with 140 followed by the transistor which combines

a total of 128 each.

4. Discussion

The figure 12 shows the criticality diagram for the system

by highlighting the criticality threshold giving priority to

certain failure modes.

Science Journal of Energy Engineering 2017; 5(2): 40-47 46

Figure 12. Criticality of the elements of the System.

Figure 13. The Pareto curve.

Analysis of system failures shows a shared distribution of

the criticality of the system elements. A threshold of two

hundred (200) has been set. Failures whose criticality is less

than 200 do not create any particular problem in that. They are

easily identifiable, are infrequent or not too bad for the system.

Criticality includes a product consisting of case, the

probability of non-detection and severity of a type of failure.

Failures whose criticality exceeds the threshold (200) deserve

special attention. This attention will result in preventive

maintenance to deal with any eventuality on.

47 Omar Ngala Sarr et al.: Analysis of Failure Modes Effect and Criticality Analysis (FMECA):

A Stand-Alone Photovoltaic System

For better visibility, a Pareto analysis was performed.

Pareto analysis

The Pareto analysis allows to see the minimum of causes

that leads to maximum effect on the system. Figure 13

represents the Pareto curve. For the draw, a ranking of failure

modes has been achieved in decreasing order, and a

percentage was calculated through the combination of

criticality.

The Pareto analysis shows that 40% of failures makes over

60% of the criticality cumulating. In this 40% we find for the

battery failures, the repetitive discharge and for photovoltaic

panels, among other failures we have the broken

interconnections. And finally to the failure modes of an

inverter may be mentioned the failure of microcontroller and

the overvoltage of the BUS.

A mastering of these failure modes (40% of the total) could

reduce by 60% the system crashes and contribute strongly to

the reliability of autonomous photovoltaic systems.

5. Conclusion

The study focused on maintenance of autonomous

photovoltaic systems. After a reminder of the exponential

law, a functional analysis of the system was made through

an octopus diagram and a fault tree. This was done in order

to help us choose the criticality coefficients through the

Failure Modes Effects and criticality Analysis (FMECA). A

Pareto study applied to the system showed that 40% of

possible failures themselves include nearly 60% of the

criticality of the system. Mastering these failures will

increase the reliability of the entire autonomous

photovoltaic system.

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